D.Phil. (Electrical & Electronic Engineering Science) / Recent advancement in optical fibre communications technology is partly due to the advancement of optical thin-film technology. The advancement of optical thin-film technology includes the development of new and existing deposition and optical filter design methods. Genetic algorithm is one of the new design methods that show promising results in designing a number of complicated design specifications. The research is entirely devoted to the investigation of the genetic algorithm design method in the design of producible polarised and non-polarised edge filters for optical fibre communication applications. In this study, a number of optical filter design methods such as Fourier Transform and refining are investigated for their potential in designing those kinds of structures. Owing to the serious limitations to which they are subject, they could not yield the kind of results anticipated. It is the finding of this study that the genetic algorithm design method, through its optimisation capability, can give reliable and producible designs. This design method, in this study, optimises the thickness of each layer to get to the best possible solution. Its capability and unavoidable limitations in designing polarised and non-polarised beam splitters, edge filters and reflectors from absorptive and dispersive materials are well demonstrated. It is observed that the optical behaviour of the non-polarised filters designed by this method show a similar trend: as the angle of incidence increases the inevitable increase in the percentage of polarisation, stop bandwidth and ripple intensity is well controlled to an acceptable level. In the case of polarised designs the S-polarised designs show a better response to the optimisation process than the P-polarised designs, but all of them are kept well within an acceptable level. It is also demonstrated that polarised and non-polarised designs from the genetic algorithm are producible with great success. This research has accomplished the task of formulating a computer program using genetic algorithm in a Mathlab® environment for the design of producible polarised and non-polarised filters from materials of absorptive and dispersive nature.

As the requirements to operate the electric power system become more stringent and operating costs must be kept to a minimum, operators and planners must ensure that power system models are accurate and capable of replicating system disturbances. Traditionally, load models were represented as static ZIP models; however, NERC has recently required that planners model the transient dynamics of motor loads to study their effect on the postdisturbance behavior of the power system. Primarily, these studies are to analyze the effects of fault-induced, delayed voltage recovery, which could lead to cascading voltage stability issues. Genetic algorithms and constrained multivariable function minimization are global and local optimization tools used to extract static and dynamic load model parameters from postdisturbance data. The genetic algorithm's fitness function minimizes the difference between measured and calculated real and reactive power by varying the model parameters. The fitness function of the genetic algorithm, a function of voltage and frequency, evaluates an individual\'s difference between measured and simulated real and reactive power. While real measured data was unavailable, simulations in PSS/E were used to create data, and then compared against estimated data to examine the algorithms' ability to estimate parameters. / Master of Science

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Optimization is considered an essential step in reinforcing the efficiency of performance and economic feasibility of construction projects. In the past few decades, evolutionary algorithms (EAs) have been widely utilized to solve various types of construction-related optimization problems due to their efficiency in finding good solutions in relatively short time periods. However, in many cases, these existing evolutionary algorithms failed to identify the optimal solution to several optimization problems. As such, it is deemed necessary to develop new approaches in order to help identify better-quality solutions. This doctoral research presents the development of a new evolutionary algorithm, named “Electimize,” that is based on the simulation of the flow of electric current in the branches of an electric circuit. The main motive in this research is to provide the construction industry with a robust optimization tool that overcomes some of the shortcomings of existing EAs. In solving optimization problems using Electimize, a number of wires (solution strings) composed of a number of segments are fabricated randomly. Each segment corresponds to a decision variable in the objective function. The wires are virtually connected in parallel to a source of an electricity to represent an electric circuit. The electric current passing through each wire is calculated by substituting the values of the segments in the objective function. The quality of the wire is based on its global resistance, which is calculated using Ohm’s law. iv he main objectives of this research are to 1) develop an optimization methodology that is capable of evaluating the quality of decision variable values in the solution string independently; 2) devise internal optimization mechanisms that would enable the algorithm to extensively search the solution space and avoid its convergence toward local optima; and 3) provide the construction industry with a reliable optimization tool that is capable of solving different classes of NP-hard optimization problems. First, internal processes are designed, modeled, and tested to enable the individual assessment of the quality of each decision variable value available in the solution space. The main principle in assessing the quality of each decision variable value individually is to use the segment resistance (local resistance) as an indicator of the quality. This is accomplished by conducting a sensitivity analysis to record the change in the resistance of a control wire, when a certain decision variable value is substituted into the corresponding segment of the control wire. The calculated local resistances of all segments of a wire are then normalized to ensure that their summation is equal to the global wire resistance and no violation is made of Kirchhoff’s rule. A benchmark NP-hard cash flow management problem from the literature is attempted to test and validate the performance of the developed approach. Not only was Electimize able to identify the optimal solution for the problem, but also it identified ten alternative optimal solutions, outperforming the existing algorithms. Second, the internal processes for the sensitivity analysis are designed to allow for extensive search of the solution space through the generation of new v wires. Every time a decision variable value is substituted in the control wire to assess its quality, a new wire that might have a better quality is generated. To further test the capabilities of Electimize in searching the solution space, Electimize was applied to a multimodal 9-city travelling salesman problem (TSP) that had been previously designed and solved mathematically. The problem has 27 alternative optimal solutions. Electimize succeeded to identify 21 of the 27 alternative optimal solutions in a limited time period. Moreover, Electimize was applied to a 16-city benchmark TSP (Ulysses16) and was able to identify the optimal tour and its alternative. Further, additional parameters are incorporated to 1) allow for the extensive search of the solution space, 2) prevent the convergence towards local optima, and 3) increase the rate of convergence towards the global optima. These parameters are classified into two categories: 1) resistance related parameters, and 2) solution exploration parameters. The resistance related parameters are: a) the conductor resistivity, b) its cross-sectional area, and c) the length of each segment. The main role of this set of parameters is to provide the algorithm with additional gauging parameters to help guide it towards the global optima. The solution exploration parameters included a) the heat factor, and b) the criterion of selecting the control wire. The main role of this set of parameters is to allow for an extensive search of the solution space in order to facilitate the identification all the available alternative optimal solutions; prevent the premature convergence towards local optima; and increase the rate of convergence towards the global optima. Two TSP instances (Bayg29 and ATT48) are attempted and vi the results obtained illustrate that Electimize outperforms other EAs with respect to the quality of solutions obtained. Third, to test the capabilities of Electimize as a reliable optimization tool in construction optimization problems, three benchmark NP-hard construction optimization problems are attempted. The first problem is the cash flow management problem, as mentioned earlier. The second problem is the time cost tradeoff problem (TCTP) and is used as an example of static optimization. The third problem is a site layout planning problem (SLPP), and represents dynamic optimization. When Electimize was applied to the TCTP, it succeeded to identify the optimal solution of the problem in a single iteration using thirty solution strings, compared to hundreds of iterations and solution strings that were used by EAs to solve the same problem. Electimize was also successful in solving the SLPP and outperformed the existing algorithm used to solve the problem by identifying a better optimal solution. The main contributions of this research are 1) developing a new approach and algorithm for optimization based on the simulation of the phenomenon of electrical conduction, 2) devising processes that enable assessing the quality of decision variable values independently, 3) formulating methodologies that allow for the extensive search of the solution space and identification of alternative optimal solutions, and 4) providing a robust optimization tool for decision makers and construction planners.

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<p> This thesis defends the use of genetic algorithms (GA) to solve the maximum number of repetitions in a binary string. Repetitions in strings have significant uses in many different fields, whether it is data-mining, pattern-matching, data compression or computational biology 14]. Main extended the definition of repetition, he realized that in some cases output could be reduced because of overlapping repetitions, that are simply rotations of one another [10]. As a result, he designed the notion of a run to capture the maximal leftmost repetition that is extended to the right as much as possible. Franek and Smyth independently computed the same number of maximum repetition for strings of length five to 35 using an exhaustive search method. Values greater than 35 were not computed because of the exponential increase in time required. Using GAs we are able to generate string with very large, if not the maximum, number of runs for any string length. The ability to generate strings with large runs is an advantage for learning more about the characteristics of these strings. </p> / Thesis / Master of Science (MSc)

A time-domain model-based crack diagnostic methodology using vibration data is presented. Most of the damage detection methods proposed to date are based on modal parameters and are limited by the loss of information caused by data reduction and by the implicit assumption of linearity. The use of time domain information permits the direct inclusion of the nonlinear behavior due to crack opening-closure cycles. In addition, very little information is lost, since no signal processing or parameter identification steps are involved. The proposed method is based on a continuous model for the transverse vibrations of beams consisting of partial differential equations of motion with varying coefficients to account for the presence of damage. In order to provide accurate representation of the structure's behavior over a broader frequency range, a new continuous cracked beam model including shear effects and rotatory inertia is developed using the Hu-Washizu-Barr variational method. The resulting equations of motion are discretized by a Galerkin method using local B-splines as test functions. The crack is assumed to be either fully open or fully closed, resulting in a bilinear system. The simultaneous identification of crack location and depth is performed by minimizing the norm of the differences between the numerical and experimental time responses to multiple excitations. Impact, low frequency sinusoidal and Schroeder--phased multisine inputs are investigated as potential excitation methods. The cost function to be minimized presents several local minima that are shown to be related to the length of the response records. A genetic algorithm is used to overcome the multimodal nature of the objective function. The methodology is validated through simulated identifications of several damage scenarios. The importance of the inclusion of the nonlinear behavior is addressed, and the effects of model uncertainties and measurement noise are quantified in terms of minimum identifiable crack size. / Ph. D.

This research tests the ability of artificial learning methodologies to map market returns better than logistic regression. The learning methodologies used are neural networks and dual-layered genetic algorithms. These methodologies are used to develop a trading strategy to generate excess returns. The excess returns are compared to test the trading strategy's effectiveness. Market-adjusted and size-adjusted excess returns are calculated. Using a trading strategy based approach the logistic regression models generated greater returns than the neural network and dual-layered genetic algorithm models. It appears that the noise in the financial markets prevents the artificial learning methodologies from properly mapping the market returns. The results confirm the findings that fundamental analysis can be used to generate excess returns. / Ph. D.

Coulomb propulsion has been proposed for spacecraft cluster applications with separation distances on the order of dozens of meters. This thesis presents an investigation of analytic charge solutions for a planar and three dimensional four satellite formations. The solutions are formulated in terms of the formation geometry. In contrast to the two and three spacecraft Coulomb formations, a four spacecraft formation has additional constraints that need to be satisfied for the individual charges on the spacecraft to be unique and real. A spacecraft must not only satisfy the previously developed inequality constraints to yield a real charge solution, but it must also satisfy three additional equality constraints to ensure the spacecraft charge is unique. Further, a method is presented to reduce the number of equality constraints arising due the dynamics of a four spacecraft formation. Formation geometries are explored to determine the feasibility of orienting a square formation arbitrarily in any given plane. The unique and real spacecraft charges are determined as functions of the orientation of the square formation in a given principal orbit plane. For a three-dimensional tetrahedron formation, the charge products obtained are a unique set of solution. The full three-dimensional rotation of a tetrahedron is reduced to a two angle rotation for simpler analysis. The number of equality constraints for unique spacecraft charges can not be reduced for a three-dimensional formation. The two angle rotation results are presented for different values of the third angle. The thesis also presents the set up for a co-linear four-craft problem. The solution for the co-linear formation is not developed. The discussion of co-linear formations serves as an open question on how to determine analytic solutions for system with null-space dimension greater than 1. The thesis also presents a numerical tool for determining potential shapes of a static Coulomb formation as a support to the analytical solutions. The numerical strategy presented here uses a distributed Genetic Algorithm (GA) as an optimization tool. The GA offers several advantages over traditional gradient based optimization methods. Distributing the work of the GA over several processors reduces the computation time to arrive at a solution. The thesis discusses the implementation of a distributed GA used in the analysis of a static Coulomb formation. The thesis also addresses the challenges of implementation of a distributed GA on a computing cluster and presents candidate solutions. / Master of Science

SPBMODEL, a legacy southern pine beetle (SPB) simulation model, was translated into a new JavaTM model called Javahog. The Javahog output was verified to be essentially identical to SPBMODEL output by means of standard and paired t-tests. Javahog was placed online and is currently accessible via a servlet. Genetic algorithms (GAs) were applied to the Javahog model. GAs are a type of optimization heuristic that operate as an analog to evolution. GAs "evolve" a very good solution to a complex problem. In this case, GAs were intended to evolve a very good version of SPBMODEL. GAs were applied in part to improve upon the SPBMODEL design, and in part to demonstrate that GAs are effective tools for recalibrating legacy simulation models. Beyond simply recalibrating model parameters, the GA was used to select optimal functional forms for the development rates of each SPB life stage. The GA evolved a model that performed better than SPBMODEL at predicting observed field data, according to a balanced fitness function and according to sums of squared errors. However, from a visual comparison of the output of both models versus observed field data, neither model achieved satisfactory performance. / Master of Science